How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
gs-quant is a quantitative finance library and financial data analytics toolkit. It serves as a framework for analyzing financial data, developing systematic trading strategies, and managing risk exposure for derivative products in global markets. The project provides tools for quantitative financial analysis, quantitative portfolio modeling, and the development of systematic trading strategies. It enables the calculation of risk for derivative products to structure and hedge positions across markets.
mlfinlab is a Python machine learning library for finance designed for building and validating models used in quantitative trading and portfolio management. It provides a financial data engineering toolkit and a quantitative strategy backtesting framework to transform raw market data into predictive signals and target classes. The library includes a synthetic financial data generator to create artificial datasets that mimic the statistical properties of real assets for stress testing. It also provides specialized tools for financial time series labeling and sampling to prevent data leakage in
FinceptTerminal is a quantitative finance platform and financial engineering library designed for asset valuation, risk management, and fixed-income analytics. It provides a comprehensive suite for algorithmic trading and investment strategy automation, integrating specialized language model agents and node-based workflows to automate market research and alpha generation. The project distinguishes itself with a dedicated game theory analysis engine for calculating Nash equilibria and simulating strategic interactions in competitive markets. It also features a specialized credit risk modeling
This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The
💸 Papers and Code Implements for Quantitative-Trading
The main features of ceruleanacg/quantitative-trading are: Quantitative Trading Strategies.
Open-source alternatives to ceruleanacg/quantitative-trading include: hudson-and-thames/mlfinlab — mlfinlab is a Python machine learning library for finance designed for building and validating models used in… llmquant/quant-wiki — quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering,… fincept-corporation/finceptterminal — FinceptTerminal is a quantitative finance platform and financial engineering library designed for asset valuation,… goldmansachs/gs-quant — gs-quant is a quantitative finance library and financial data analytics toolkit. It serves as a framework for… je-suis-tm/quant-trading — This project is a Python financial analytics framework and quantitative trading library. It provides a suite of… carlos8f/zenbrain — A framework for machine-learning bots.